Components for WormTensor object

Description

These are generic methods for WormTensor

Usage

worm_membership(object, k)

worm_clustering(
  object,
  algorithm = c("MCMI", "OINDSCAL", "CSPA"),
  num.iter = 30,
  thr = 1e-10,
  verbose = FALSE
)

worm_evaluate(object, labels = NULL)

worm_visualize(
  object,
  out.dir = tempdir(),
  algorithm = c("tSNE", "UMAP"),
  seed = 1234,
  tsne.dims = 2,
  tsne.perplexity = 15,
  tsne.verbose = FALSE,
  tsne.max_iter = 1000,
  umap.n_neighbors = 15,
  umap.n_components = 2,
  silhouette.summary = FALSE
)

Arguments

S4 class used by as_worm_tensor.R, worm_membership.R, worm_clustering.R, worm_evaluate.R, worm_visualize.R

Description

S4 class used by as_worm_tensor.R, worm_membership.R, worm_clustering.R, worm_evaluate.R, worm_visualize.R

Slots

dist_matrices

list

n_animals

numeric

union_cellnames

character

n_union_cells

numeric

membership_tensor

Tensor

k

numeric

clustering_algorithm

character

clustering

numeric

weight

numeric

factor

matrix

consensus

matrix

eval

list

dimension_reduction_algorithm

character

Generates WormTensor object A WormTensor object is generated from distance matrices.

Description

Generates WormTensor object A WormTensor object is generated from distance matrices.

Usage

as_worm_tensor(Ds)

Arguments

Value

An object containing distance matrices and metadata

Examples

    worm_download("mSBD", qc = "PASS")$Ds |> as_worm_tensor() -> object

Generates clustering result A clustering result is generated from a membership tensor.

Description

Generates clustering result A clustering result is generated from a membership tensor.

Usage

## S4 method for signature 'WormTensor'
worm_clustering(
  object,
  algorithm = c("MCMI", "OINDSCAL", "CSPA"),
  num.iter = 30,
  thr = 1e-10,
  verbose = FALSE
)

Arguments

Value

WormTensor object with a clustering result added

Examples

    # Pipe Operation
    worm_download("Euclid", qc = "WARN")$Ds |>
        as_worm_tensor() |>
            worm_membership(k = 6) -> object
    worm_clustering(object, verbose = TRUE) -> ob_mcmi
    worm_clustering(object, algorithm = "OINDSCAL", verbose = TRUE) -> ob_oind
    worm_clustering(object, algorithm = "CSPA", verbose = TRUE) -> ob_cspa

Generates distance matrices Distance matrices are generated between the cells of multiple animals (Caenorhabditis elegans) from time-series matrices.

Description

Generates distance matrices Distance matrices are generated between the cells of multiple animals (Caenorhabditis elegans) from time-series matrices.

Usage

worm_distance(data, distance = c("mSBD", "SBD", "Euclid"))

Arguments

Value

A list containing distance matrices

Examples

# Toy data
n_cell_x <- 13
n_cell_y <- 24
n_cell_z <- 29
n_cells <- 30
n_time_frames <- 100

# 13 cells, 100 time frames
animal_x <- matrix(runif(n_cell_x * n_time_frames),
    nrow = n_cell_x, ncol = n_time_frames
)
rownames(animal_x) <- sample(seq(n_cells), n_cell_x)
colnames(animal_x) <- seq(n_time_frames)

# 24 cells, 100 time frames
animal_y <- matrix(runif(n_cell_y * n_time_frames),
    nrow = n_cell_y, ncol = n_time_frames
)
rownames(animal_y) <- sample(seq(n_cells), n_cell_y)
colnames(animal_y) <- seq(n_time_frames)

# 29 cells, 100 time frames
animal_z <- matrix(runif(n_cell_z * n_time_frames),
    nrow = n_cell_z, ncol = n_time_frames
)
rownames(animal_z) <- sample(seq(n_cells), n_cell_z)
colnames(animal_z) <- seq(n_time_frames)

# Positive Control of Difference between SBD and mSBD
animal_z[2, ] <- -animal_x[1, ]
X <- list(
    animal_x = animal_x,
    animal_y = animal_y,
    animal_z = animal_z
)
Ds_mSBD <- worm_distance(X, "mSBD")

Downloads distance matrices 28 animals' data including 24 normal and 4 noisy are retrieved from figshare.

Description

Downloads distance matrices 28 animals' data including 24 normal and 4 noisy are retrieved from figshare.

Usage

worm_download(distance = c("mSBD", "Euclid"), qc = c("PASS", "WARN", "FAIL"))

Arguments

Value

A List of containing distance matrices. The list also includes metadata for each animals.

Examples

    Ds_Euclid <- worm_download("Euclid", qc = "WARN")
    Ds_mSBD <- worm_download("mSBD", qc = "PASS")

Evaluates clustering result An evaluation result is generated from a WormTensor object.

Description

Evaluates clustering result An evaluation result is generated from a WormTensor object.

Usage

## S4 method for signature 'WormTensor'
worm_evaluate(object, labels = NULL)

Arguments

Value

WormTensor object with an evaluation result added

Examples

    # Pipe Operation
    worm_download("mSBD", qc = "PASS")$Ds |>
        as_worm_tensor() |>
            worm_membership(k = 6) |>
                worm_clustering() -> object
     # Internal evaluation
     worm_evaluate(object) -> object_internal

     # External evaluation by sample labels
     labels <- list(
         label1 = sample(3, length(object@clustering), replace = TRUE),
         label2 = sample(4, length(object@clustering), replace = TRUE),
         label3 = sample(5, length(object@clustering), replace = TRUE)
     )
     worm_evaluate(object, labels) -> object_external

     # External evaluation by worm_download labels
     Ds_mSBD <- worm_download("mSBD", qc = "PASS")
     labels <- list(
         label1 = replace(
             Ds_mSBD$labels$Class,
             which(is.na(Ds_mSBD$labels$Class)),
             "NA"
         ),
         label2 = sample(4, length(object@clustering), replace = TRUE),
         label3 = sample(5, length(object@clustering), replace = TRUE)
     )
     worm_evaluate(object, labels) -> object_external_Class

Generates membership tensor A membership tensor is generated from distance matrices.

Description

Generates membership tensor A membership tensor is generated from distance matrices.

Usage

## S4 method for signature 'WormTensor'
worm_membership(object, k)

Arguments

Value

WormTensor object with membership tensor added

Examples

    # Pipe Operation
    worm_download("mSBD", qc = "PASS")$Ds |>
        as_worm_tensor() -> object
    # k=6
    worm_membership(object, k = 6) -> object_k6

Plots evaluation result A visualization result is generated from a WormTensor object.

Description

Plots evaluation result A visualization result is generated from a WormTensor object.

Usage

## S4 method for signature 'WormTensor'
worm_visualize(
  object,
  out.dir = tempdir(),
  algorithm = c("tSNE", "UMAP"),
  seed = 1234,
  tsne.dims = 2,
  tsne.perplexity = 15,
  tsne.verbose = FALSE,
  tsne.max_iter = 1000,
  umap.n_neighbors = 15,
  umap.n_components = 2,
  silhouette.summary = FALSE
)

Arguments

Value

Silhouette plots. ARI with a merge result and each animal(with MCMI). Dimensional reduction plots colored by cluster, no. of identified cells, consistency(with labels), Class_label(with labels).

References

The .dist_nn function is quoted from dist_nn (not exported function) in package uwot(https://github.com/jlmelville/uwot/tree/f467185c8cbcd158feb60dde608c9da153ed10d7).

Examples

    # Temporary directory to save figures
    out.dir <- tempdir()

    # Labels
    worm_download("mSBD", qc = "PASS")$Ds |>
        as_worm_tensor() |>
            worm_membership(k = 6) |>
                worm_clustering() -> object
    Ds_mSBD <- worm_download("mSBD", qc = "PASS")
    labels <- list(
        label1 = replace(
            Ds_mSBD$labels$Class,
            which(is.na(Ds_mSBD$labels$Class)),
            "NA"
        ),
        label2 = sample(4, length(object@clustering), replace = TRUE),
        label3 = sample(5, length(object@clustering), replace = TRUE)
    )

    # Pipe Operation (without Labels)
    worm_download("mSBD", qc = "PASS")$Ds |>
        as_worm_tensor() |>
            worm_membership(k = 6) |>
                worm_clustering() |>
                    worm_evaluate() |>
                        worm_visualize(out.dir) -> object_no_labels

    # Pipe Operation (with Labels)
    worm_download("mSBD", qc = "PASS")$Ds |>
        as_worm_tensor() |>
            worm_membership(k = 6) |>
                worm_clustering() |>
                    worm_evaluate(labels) |>
                        worm_visualize(out.dir) -> object_labels